Evaluating the ecological vulnerability of Chongqing using deep learning.

Area of Science:

• Environmental Science
• Geospatial Analysis
• Artificial Intelligence

Background:

• Ecological vulnerability assessments are crucial for environmental protection and governance.
• Traditional methods may not fully capture complex ecological interactions.
• Deep learning offers advanced capabilities for analyzing large-scale environmental data.

Purpose of the Study:

• To evaluate the ecological vulnerability of Chongqing, China using deep learning.
• To generate high-resolution ecological vulnerability maps for targeted conservation.
• To provide a reference for future deep learning-based ecological studies.

Main Methods:

• Screening 16 ecological vulnerability factors using information gain ratio.
• Developing ecological vulnerability models with Deep Neural Network (DNN) and Convolutional Neural Network (CNN).
• Utilizing Random Forest to determine factor importance and validate model performance.

Main Results:

• Both DNN and CNN models demonstrated high fitting accuracy with small errors.
• The CNN model achieved a superior Area Under the Curve (AUC) of 0.926 compared to DNN's 0.888.
• Karst topography was identified as the primary driver of ecological vulnerability, exacerbated by human activities.

Conclusions:

• Deep learning, especially CNNs, is a viable tool for ecological vulnerability assessment.
• Generated vulnerability maps align with field observations, identifying high-risk areas in karst regions and valleys.
• Addressing issues like poor forest quality, desertification, and soil erosion is vital for mitigating ecological vulnerability.